Building Simulation

, Volume 11, Issue 3, pp 533–547 | Cite as

A novel cost-optimizing demand response control for a heat pump heated residential building

  • Vahid Arabzadeh
  • Behrang Alimohammadisagvand
  • Juha Jokisalo
  • Kai Siren
Research Article Building Systems and Components
  • 47 Downloads

Abstract

The present article describes the integration of a data-driven predictive demand response control for residential buildings with heat pump and on-site energy generation. The data driven control approach schedules the heating system of the building. In each day, the next 24 hours heating demand of buildings, including space heating and domestic hot water consumption, are predicted by means of a hybrid wavelet transformation and a dynamic neural network. Linear programming is implemented to define a cost-optimal schedule for the heat pump operation. Moreover, the study discusses the impact of heat demand prediction error on performance of demand response control. In addition, the option of energy trading with the electrical grid is considered in order to evaluate the possibility of increasing the profit for private householders through on-site energy generation. The results highlight that the application of the proposed predictive control could reduce the heating energy cost up to 12% in the cold Finnish climate. Furthermore, on-site energy generation declines the total energy cost and consumption about 43% and 24% respectively. The application of a data-driven control for the demand prediction brings efficiency to demand response control.

Keywords

demand response nonlinear autoregressive with exogenous inputs wavelet transform optimal predictive control photovoltaic system heat pump 

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Notes

Acknowledgements

The authors gratefully acknowledge the partial funding provided by the SAGA-project that belongs to the Aalto Energy Efficiency Research Program (AEF) of Aalto University and the Fortum Foundation.

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Copyright information

© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  • Vahid Arabzadeh
    • 1
  • Behrang Alimohammadisagvand
    • 1
  • Juha Jokisalo
    • 1
  • Kai Siren
    • 1
  1. 1.HVAC Technology, Department of Mechanical engineering, School of EngineeringAalto UniversityAaltoFinland

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